Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

TL;DR

This paper introduces novel matrix completion and joint sparsity recovery algorithms for collaborative spectrum sensing in cognitive radio networks, significantly reducing sensing and reporting requirements while accurately detecting spectrum holes.

Contribution

It proposes new algorithms that enable exact spectrum detection from sparse, incomplete reports, improving efficiency over traditional methods in cognitive radio networks.

Findings

Matrix completion achieves exact detection with ≤50% samples in small networks.

Joint sparsity recovery performs well in large-scale networks.

Numerical results confirm robustness and effectiveness of the proposed methods.

Abstract

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.